中文摘要：

针对当前目标跟踪算法鲁棒性低且运算慢的问题，本文提出了一种基于子空间学习的实时目标跟踪算法。该方法在粒子滤波跟踪框架下，采用增量式PCA子空间学习方法学习一个正交子空间，利用学习到的正交子空间对目标外观进行线性表示；针对目标在遮挡、运动模糊等复杂干扰状态下容易产生跟踪漂移的问题，本文建立了一个将遮挡等复杂因素考虑在内的观测模型和模板更新方案，解决了基于最小均方误差准则的传统观测模型在复杂场景下的跟踪漂移问题。实验结果表明，本文的跟踪方法能够达到很高的跟踪精度，同时也达到了接近实时的跟踪速度。

英文摘要：

Because of the poor efficiency and effectiveness of current visual tracking algorithms, a real-time object tracking algorithm is proposed based on subspace learning.Under the framework of particle filtering, this paper uses the incremental PCA subspace method to learn an orthogonal subspace, and then get the linear representation of target appearance. In order to avoid the tracking drift produced by complicated interference, such as occlusions, motion blur and so on, an observation model and a template update scheme are built, which consider the complicated interference especially occlusions, to solve the drift problem of the traditional observation model based onminimum mean square error. The experimental results show that the algorithm in complicated conditions can be well implemented compared with several state-of-the-art algorithms.